R/readGenotypes.R

In AndersenLab/easyfulcrum: easyFulcrum: An R package to process and analyze ecological sampling data generated using the Fulcrum mobile application

#' readGenotypes
#'
#' \code{readGenotypes} reads genotyping data from project specific genotyping
#' Google sheets.
#'
#' @param gsKey a vector containing the google sheet keys for the genotype data
#'   to load. Your Google Sheet keys are found in your Google sheets URL. Select
#'   the string found between the slashes after spreadsheets/d in your Google
#'   Sheet URL.
#' @param col_types a single string of readr-style short codes. Default is to
#'   let the column classes be guessed \code{col_types = "?"}.
#'   See details here:
#'   https://googlesheets4.tidyverse.org/reference/read_sheet.html
#'   If using the nematode genotyping template, the correct \code{col_types} are
#'   "cDDdcdcddddddDcDDdcdcdddddddcdcccddccc". If using the general genotyping
#'   template, the correct  \code{col_types} are "cccdcc". If using a customized
#'   genotyping sheet the user can specify the correct column classes or allow
#'   the fucnction to guess.
#' @return A dataframe generated from the Google sheets \code{gsKey} argument.
#'   If multiple Google sheets are provided the data are appended using rbind.
#'   Note, the genotyping sheet must contain variable names specified by the
#'   \code{wild_isolate_genotyping_template} and the data must be found on the
#'   sheet named \code{genotyping template}.
#' @import dplyr
#' @import googlesheets4
#' @export
#'

readGenotypes <- function(gsKey, col_types = "?") {
  # read genotyping sheet(s)
  genotyping_sheet <- NULL

  for(i in unique(gsKey)){
    # get data from sheet
    geno <- googlesheets4::read_sheet(i, range = "genotyping template", col_types = col_types) %>%
      dplyr::filter(!is.na(project_id))

    genotyping_sheet <- rbind(genotyping_sheet, geno)
  }
  # return raw genotyping sheet
  return(genotyping_sheet)
}